Mapping Sustainability to Cybernetic-Generative Artificial Intelligence-Based Education: An Innovative Neutrosophic Orbifold-Lattice Methodology

The lightning-fast development of artificial intelligence (AI), notably generative artificial intelligence (Gen AI) technologies, has intrigued multiple disciplines, particularly education. In this context, Large Language Models (LLMs) serve as beneficial cognitive resources that address knowledge deficits and provide tailored educational support for learners and staff. For learners, LLMs are regarded as knowledgeable educators who offer prompt, focused responses and rationales to tricky queries. Whereby LLMs for staff, Strength enhancer, automating tedious tasks, and creating intelligent resources.Gen AI’s rapid growth offers enormous obstacles for educational systems, compelling them to discover solutions for these obstacles. Conceptually, cybernetics is leveraged for bridging the gap between Gen AI and the practice of education. Thereby, the objective of this study is to merge cybernetic with Gen AI (cyber-Gen AI) and showcase the relation between this merger and sustainability, resilience, and cognition. This objective is achieved through leveraging this merger in education systems to achieve sustainability and smartness. Practically, evaluating the role of cyber-Gen AI in education systems is imperative. Hence, a novel model of a Neutrosophic Orbifold-Lattice (NOL) model is proposed in this study. Experimentally, the innovative model is applied in a real case study to verify its accuracy in making decisions.

Authors

Publication Details

Journal
Neutrosophic Systems with Applications
Published
2025-12-22
DOI
https://doi.org/10.63689/2993-7159.1313
Primary Topic
Cognitive Computing and Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Mapping Sustainability to Cybernetic-Generative Artificial Intelligence-Based Education: An Innovative Neutrosophic Orbifold-Lattice Methodology

Ahmed A. Metwaly, Mona Mohamed
Neutrosophic Systems with Applications
Cognitive Computing and Networks
article

Mapping Sustainability to Cybernetic-Generative Artificial Intelligence-Based Education: An Innovative Neutrosophic Orbifold-Lattice Methodology

Ahmed A. Metwaly, Mona Mohamed
article en

Abstract

The lightning-fast development of artificial intelligence (AI), notably generative artificial intelligence (Gen AI) technologies, has intrigued multiple disciplines, particularly education. In this context, Large Language Models (LLMs) serve as beneficial cognitive resources that address knowledge deficits and provide tailored educational support for learners and staff. For learners, LLMs are regarded as knowledgeable educators who offer prompt, focused responses and rationales to tricky queries. Whereby LLMs for staff, Strength enhancer, automating tedious tasks, and creating intelligent resources.Gen AI’s rapid growth offers enormous obstacles for educational systems, compelling them to discover solutions for these obstacles. Conceptually, cybernetics is leveraged for bridging the gap between Gen AI and the practice of education. Thereby, the objective of this study is to merge cybernetic with Gen AI (cyber-Gen AI) and showcase the relation between this merger and sustainability, resilience, and cognition. This objective is achieved through leveraging this merger in education systems to achieve sustainability and smartness. Practically, evaluating the role of cyber-Gen AI in education systems is imperative. Hence, a novel model of a Neutrosophic Orbifold-Lattice (NOL) model is proposed in this study. Experimentally, the innovative model is applied in a real case study to verify its accuracy in making decisions.

Neutrosophic Systems with ApplicationsVol. 25(12)
Openalex Percentile: Top 18%
Cognitive Computing and Networks
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